Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/350778
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampSilva, Victor Zucatti da-
dc.contributor.authorunicampLui, Hugo Felippe da Silva-
dc.contributor.authorunicampPitz, Diogo Berta-
dc.contributor.authorunicampWolf, William Roberto-
dc.typeArtigopt_BR
dc.titleAssessment of reduced-order modeling strategies for convective heat transferpt_BR
dc.contributor.authorZucatti, Victor-
dc.contributor.authorLui, Hugo F. S.-
dc.contributor.authorPitz, Diogo B.-
dc.contributor.authorWolf, William R.-
dc.subjectAvaliaçãopt_BR
dc.subjectModelagempt_BR
dc.subjectCalor - Transmissãopt_BR
dc.subject.otherlanguageEvaluationpt_BR
dc.subject.otherlanguageModelingpt_BR
dc.subject.otherlanguageHeat - Transmissionpt_BR
dc.description.abstractAn assessment of physics-based and data-driven reduced-order models (ROMs) is presented for the study of convective heat transfer in a rectangular cavity. Despite the simple geometrical configuration, the current setup offers increasingly rich dynamics as the thermal forcing is increased, thus making it a suitable candidate to evaluate the performance of ROMs. First, flow simulations are performed using a high-order spectral element method that will feed the ROMs with well-resolved temporal and spatial information. Proper orthogonal decomposition (POD) is applied to reduce the problem dimensionality for all models. The class of tested physics-based models include the Galerkin and least-squares Petrov–Galerkin (LSPG) methods that rely on projection of the Navier–Stokes and energy equations being solved. On the other hand, the data-driven methods applied in this work rely on regression of the governing equations, which are treated as a nonlinear dynamical system. The data-driven methods tested here include the sparse identification of nonlinear dynamics (SINDy) approach and a method recently proposed in literature based on deep neural networks (DNNs). All ROMs are able to represent the periodical temporal dynamics of a low Rayleigh number flow. However, the physics-based approaches demonstrate a better performance for a moderate Rayleigh number case with more complex flow dynamics, when several frequencies are excited in a non-periodical fashionpt_BR
dc.relation.ispartofNumerical heat transfer Part A : applicationspt_BR
dc.publisher.cityNew York, NYpt_BR
dc.publisher.countryEstados Unidospt_BR
dc.publisherTaylor & Francispt_BR
dc.date.issued2020-
dc.date.monthofcirculationApr.pt_BR
dc.language.isoengpt_BR
dc.description.volume77pt_BR
dc.description.issuenumber7pt_BR
dc.description.firstpage702pt_BR
dc.description.lastpage729pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn1040-7782pt_BR
dc.identifier.eissn1521-0634pt_BR
dc.identifier.doi10.1080/10407782.2020.1714330pt_BR
dc.identifier.urlhttps://www.tandfonline.com/doi/full/10.1080/10407782.2020.1714330pt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsorshipFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPpt_BR
dc.description.sponsordocumentnumberNão tempt_BR
dc.description.sponsordocumentnumber2013/08293-7; 2013/07375-0; 2018/11410-9; 2019/18809-7pt_BR
dc.date.available2020-10-08T18:54:25Z-
dc.date.accessioned2020-10-08T18:54:25Z-
dc.description.provenanceSubmitted by Susilene Barbosa da Silva (susilene@unicamp.br) on 2020-10-08T18:54:25Z No. of bitstreams: 0. Added 1 bitstream(s) on 2021-01-07T20:40:44Z : No. of bitstreams: 1 000511589900001.pdf: 5056783 bytes, checksum: 97fbf3c4deafb34c281f3a8affe2c4b3 (MD5) Bitstreams deleted on 2021-01-08T14:10:47Z: 000511589900001.pdf,. Added 1 bitstream(s) on 2021-01-08T14:14:19Z : No. of bitstreams: 1 000511589900001.pdf: 5056783 bytes, checksum: 97fbf3c4deafb34c281f3a8affe2c4b3 (MD5) Bitstreams deleted on 2021-01-13T13:27:26Z: 000511589900001.pdf,. Added 1 bitstream(s) on 2021-01-13T13:29:56Z : No. of bitstreams: 1 000511589900001.pdf: 5056783 bytes, checksum: 97fbf3c4deafb34c281f3a8affe2c4b3 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-10-08T18:54:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2020en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/350778-
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.departmentDepartamento de Energiapt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.subject.keywordConvectivept_BR
dc.identifier.source000511589900001pt_BR
dc.creator.orcidSem informaçãopt_BR
dc.creator.orcid0000-0001-7357-7909pt_BR
dc.creator.orcid0000-0002-4277-881Xpt_BR
dc.creator.orcid0000-0001-8207-8466pt_BR
dc.type.formArtigo originalpt_BR
dc.description.sponsorNoteThe authors of this work would like to acknowledge Fundac¸~ao de Amparo a Pesquisa do Estado de S~ao Paulo, FAPESP, for supporting the present work under research grants No. 2013/08293-7 and 2013/07375-0. The first author is supported by FAPESP scholarships No. 2018/11410-9 and 2019/18809-7, and the second author is supported by a scholarship from Conselho Nacional de Desenvolvimento Cient ıfico, CNPqpt_BR
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